For exam-ple, the position of a phrase (measured by the number of words before its first appearance divided by the document length) is very useful for news article text, since keywords often appear early in the document (e.g., in the first paragraph).

for the less well structured meeting domain (lack of title and paragraph), these kinds of features may not be indicative.


For the graph-based methods, we notice that adding POS filtering also improves performance, similar to the TFIDF framework.

the graph method does not perform as well as the TFIDF approach.


Compared to the supervised results, the TFIDF approach is worse in terms of the individual maximum F-measure, but achieves similar performance when using the weighted relative score.

the unsupervised TFIDF approach is much simpler and does not require any annotated data for training.


A more principled approach to setting the costs would be to estimate from perceptual experiments or user studies what the impact of remaining in gap or overlap is compared to that of a cut-in or false interruption.

as a first approximation, the proposed cost structure offers a simple way to take into account some of the constraints of interaction.


The default decision boundary for this tagging task is 0.5 posterior probability (more likely than not), and tagging performance at that threshold is good (around 97% accuracy, as mentioned previously).

since this is a pre-processing step, we may want to reduce possible cascading errors by allowing more words into the sets B and E. In other words, we may want more precision in our set exclusion constraints.


Since there is no work required for these cells, the amount of work required to parse the sentence is reduced.

the quadratic bound does not include any potential reduced work in the remaining open cells.


For example, categories populating the cell spanning abc in position (1, 3) can be built in two ways: either by combining entries in cell (1, 1) with entries in (2, 3) at midpoint m = 1; or by combining entries in (1, 2) and (3, 3) at midpoint m = 2.

cell (1, 2) is closed, hence there is only one midpoint at which (1, 3) can be built (m = 1).


That mode of operation is useful for any model which purports to be potentially extensible to speech recognition or to model the human speech processor.

top-down parsers require exhaustive searches, meaning that they need to explore interpretations containing disfluency, even in the absence of syntactic cues for its existence.


This makes an assumption that repairs are always maximally local, which probably does not hurt accuracy, since most repairs actually are quite short.

this assumption is obviously not true in the general case, since in Figure 3 for example, the repair could trace all the way back to the S label at the root of the tree in the case of a restarted sentence.


Similar problems exist more widely throughout natural language processing where greedy based methods and heuristic beam search have been used in lieu of exact methods.

recently there has been an increasing interest in using Integer Linear Programming (ILP) as a means to find MAP solutions.


(2004) only consider sentences of up to eight tokens.

recent work (Riedel and Clarke, 2006) has shown that even exponentially large decoding problems may be solved efficiently using ILP solvers if a Cutting-Plane Algorithm (Dantzig et al., 1954) is used.


A simple log-linear form is used in SMT systems to combine feature functions designed for identifying good translations, with proper weights.

we often observe that tuning the weight associated with each feature function is indeed not easy.


Practically, as also shown in our experiments, we observe simplex-downhill usually gives better solutions over MER with random restarts for both, and reaches the solutions much faster in most of the cases.

simplex-downhill algorithm is an unconstrained algorithm, which does not leverage any domain knowledge in machine translation.


On our devset, we also observed that whenever optimizing toward TER (or mixture of TER & BLEU), MER does not seem to move much, as shown in Figure 1-(a) and Figure 1-(d).

on BLEU (NIST or IBM version), MER does move reasonably with random restarts.


One type regards words which refer specifically to the category name's meaning, such as pitcher for the category Baseball.

typical context words for the category which do not necessarily imply its specific meaning, like stadium, also come up as similar to baseball in LSA space.


It can be seen from Table 1 that using purely unigram features, the accuracy obtained is not any better than the majority classifier for qualified vs. bald distinction.

the Part-of-Speech bigram features and the not-in-scope features achieve a marginally significant improvement over the unigrams-only baseline.


Rubin et al (2008), for example, propose an ontology and annotation tool for semantic annotation of image regions in radiology.

creating a dataset of image regions manually annotated and delineated by domain experts, is a costly enterprise.


In the GMM mean supervector space, a naturally arising distance metric is the Euclidean distance metric.

it is observed that the supervectors show strong directional scattering patterns.


These compound classes are considered in the decoding process then projected back afterwards to recover the two types of frame↔FE connections.

some links are lost because decoding is sequential.


This task was quite simple, with glosses amenable to Web approaches, and is promising for automatically extending the coverage of a Malay lexicon.

we expect that the Malay glosses will block readings of Indonesian classifiers, and classifiers in other languages will require different strategies; we intend to examine this in future work.


The baseline algorithm has been found to be very useful in automatic speech recognition of agglutinative languages (Kurimo et al., 2006).

it often oversegments morphemes that are rare or not seen at all in the training data.


The communicative implications of accenting influence the interpretation of a word or phrase.

the acoustic excursions associated with accent are typically aligned with the lexically stressed syllable of the accented word.


if we accept that the feature representations accurately model the acoustic information contained in the regions of analysis and that the BURNC annotation is accurate, the most likely ex-planation for the superiority of word-based prediction over syllable-or vowel-based strategiesis is that the acoustic excursions correlated with accent occur outside a word's lexically stressed syllable.


(2008) using syntactic and acoustic components.

our experiments use only acoustic features, since we are concerned with comparing domains of acoustic analysis within the larger task of accent identification.


In fact, our corresponding result on the FrameNet corpus (Table 2) is P =0.784, R=0.571, F 1 =0.661, where the corpus contains much more data, its sentences come from a standard written text (no disfluencies are present) and it is in English language, which is morphologically simpler than Italian.

the Italian corpus includes optimal syntactic annotation which exactly fits the frame semantics, and the number of frames is lower than in the FrameNet experiment.


In the EBDM framework for task-oriented dialogs, agenda graph is manually designed to address two aspects of a dialog management: (1) Keeping track of the dialog state with a view to ensuring steady progress towards task completion, and (2) Supporting n-best recognition hypotheses to improve the robustness of dialog manager.

manually building such graphs for various applications may be labor intensive and time consuming.


Such a non-linear phenomenon can be implicitly captured by using the kernel trick.

its computational cost is very high, not only during training but also at inference time.


Voice Search applications provide a very convenient and direct access to a broad variety of services and information.

due to the vast amount of information available and the open nature of the spoken queries, these applications still suffer from recognition errors.


Automatic summarization has historically focused on summarizing events, a task embodied in the series of Document Understanding Conferences 1 .

there has also been work on entity-centric summarization, which aims to produce summaries from text collections that are relevant to a particular entity of interest, e.g., product, person, company, etc.


(2006) explores retrieval systems that align query results to highlight points of commonality and difference.

we attempt to identify contrasts from the data, and then generate summaries that highlight them.


The highest scoring contrastive pair of summaries would consist of one for x that mentions a exclusively, and one for y that mentions b exclusively -these summaries each mention a promiment aspect of their product, and have no overlap with each other.

they provide a false contrast because they each attempt to contrast the other summary, rather than the other product.


Therefore, our next algorithm aims to set the PA based on the ratio of neg to pos instances in the entire corpus.

since we don't have labels for the entire corpus, we don't know this ratio.


Syntax-based MT systems have proven effective-the models are compelling and show good room for improvement.


It is not clear that a decoder such as ours, without the source-tree constraint, would benefit from this method, as building a context-free forest consistent with future language model integration via cubes is expensive on its own.

we see potential integration of both methods in two places: First, the merge-lists algorithm can be used to lazily process any nested for-loops-including vanilla CKYprovided the iterands of the loops can be prioritized.


Thus, these policies have similar motivation to the syntactic features in the McDonald (2006) model.

there is a fundamental difference in the way these policies are computed.


When the English speaker points towards himself, the system will switch to English-Iraqi translation.

when the Wii is pointed towards somebody else, the system will switch to Iraqi-English translation.


The previous results indicate that we require human translation references on day 1 data to get improved performance on day 2.

our goal is to make a better system on day 2 but try to minimize human efforts on day 1.


Our preliminary results show that coreferring on the basis of just one special word and one named entity for those names in "low" or "very low" does not lose more than 1,5% in precision, while it gains up to 40% in recall for these cases.

for "very high" perplexity two-token names we were able to increase precision by requiring a stronger similarity between contexts.


It is less likely that newer web pages from "sigir2009" can be ranked higher using features that implicitly favor old pages.

the fundamental problem is that current approaches have focused on improving general ranking algorithms.


For the simplest approach, the same threshold value, θ, can be applied to all the patterns.

we assumed that each pattern has its own optimal threshold value as its own confidence score, which is different from other patterns' threshold values.


(2008) addressed the 1 http://www.nist.gov/speech/tests/ace/ same task as we did in this paper.

to our knowledge, the language specific issue and feature contributions for Chinese event extraction have not been reported by earlier researchers.


Thus, segmentation is usually an indispensible step for further processing, e.g., Part-of-Speech tagging, parsing, etc.

the segmentation may cause a problem in some tasks, e.g., name entity recognition (Jing et al., 2003) and event trigger identification.


Comparing to the Chinese event extraction system reported by (Tan et al., 2008), our scores are much lower.

we argue that we apply much more strict evaluation metrics.


Despite of the multi-topic nature of the speeches, differences in training and test perplexities indicate that the topics in test are well represented in the training set (corpus statistics in Table 1).

the Protocols corpus is a collection of medical protocols.


A given translation is considered to be suitable if it can be manually post-edited with effort savings, i.e., the evaluator thinks that a manual post-editing will increase his productivity.

if the evaluator prefers to ignore the proposed translation and start it over, the sentence is deemed not suitable.


With respect to the Protocols corpus, as expected, results were found not so satisfactory.

human translators find themselves these documents complex.


All data sets were case-sensitive with punctuation marks preserved.

in a real-world application, identical language resources covering three or more languages are not necessarily to be expected.


For these six languages, all language pair combinations achieved the highest scores using the English pivot translation approach.

english is the pivot language of choice for only 16.2% (11 out of 68) of the language pairs when translating from/into Japanese, Korean, Indonesian, or Malay.


For Chinese, the choice of the optimal pivot language varies largely depending on the language direction.

the selection of the optimal pivot language is not symmetric for 34.5% of the language pairs, i.e., a different optimal pivot language was obtained for the SRC-TRG compared to the TRG-SRC translation task.


Thus, the classifier which determines the precedence relation is not enough.

an adequate rule can be inferred with an additional classifier trained to find good starting points: a temporal adjunct may appear as the first constituent in a sentence; if it is not chosen for this position, it should be preceded by the pronominalized subject (she), the indirect object (him) and the short non-pronominalized object (an email).


In English, the positions of required elements of a sentence, verb phrase or noun phrase are relatively fixed.

many sentences also include adverbials whose position is not fixed (Figure 1).


Since our main focus is on ro- bustness to speech recognition errors, our data set is limited to those questions that are worded very similarly to the candidate answers.

the approach is more general, and can be extended to tackle both challenges.


We have focussed principally on understanding meetings in terms of their lexical content, augmented by various multimodal streams.

in many interactions, the social signals are at least as important as the propositional content of the words (Pentland, 2008); it is a major challenge to develop meeting interpretation components that can infer and take advantage of such social cues.


The AMI corpus involved a substantial effort from many individuals, and provides an invaluable resource.

we do not wish to do this again, even if we are dealing with a domain that is significantly different, such as larger groups, or family "meetings".


It should be noted here that our focus is on improving parsing performance using a single underlying grammar class, which is somewhat orthogonal to the issue of parser combination, that has been studied elsewhere in the literature (Sagae and Lavie, 2006;Fossum and Knight, 2009;Zhang et al., 2009).

to that line of work, we also do not restrict ourselves to working with kbest output, but work directly with a packed forest representation of the posteriors, much in the spirit of Huang (2008), except that we work with several forests rather than rescoring a single one.


Our model is reminiscent of Logarithmic Opinion Pools (Bordley, 1982) and Products of Experts (Hinton, 2001).

3 because we believe that none of the underlying grammars should be favored, we deliberately do not use any combination weights.


In the example, one of the underlying grammars (G 1 ) had an imperfect recall score, because of its preference for flat structures (it missed an NP node in the second part of the sentence).

the other grammar (G 2 ) favors deeper structures, and therefore introduced a superfluous ADVP node.


Maximizing the expected number of correct productions is superior for F 1 score (see the one grammar case in Figure 6).

as to be expected, likelihood is better for exact match, giving a score of 47.6% vs. 46.8%.


They also demonstrate that training their model on WSJ allows them to accurately predict parsing accuracy on the BROWN corpus.

our models are trained over multiple domains to model which factors influence cross-domain performance.


10 The sentences were chosen randomly, so each one is potentially from a different domain.

bNC can be thought of as its own domain in that it contains significant lexical differences from the American English used in our other corpora.


Prosodic information such as pause length, duration of words and phones, pitch contours, energy contours, and their normalized values have been used in speech processing tasks like sentence boundary detection .

other researchers use linguistic encoding schemes like ToBI (Silverman et al., 1992), which encodes tones, the degree of juncture between words, and prominence symbolically.


If the sub-tree is constructed from a binary rule rewrite X → Y Z, then the root nonterminal Y of some best sub-tree over some span (i, k) will generate break b k because Y is the highest nonterminal that covers word w k as the right-most terminal 4 .

the root nonterminal Z of some best subtree over some span (k+1, j) can not generate break b j because Z has a higher nonterminal X that also covers word w j as its right-most terminal.


The DIRECT bars represent direct parsing results for models trained and evaluated on the original data, ORA-CLE bars for models trained and evaluated on the modified oracle data (see Subsection 6.2), and the ORACLE-RESCORE and DIRECTRESCORE bars for results of the two rescoring approaches (described in Subsection 6.3) on the original evaluation data.

the prosodically enriched methods do not significantly improve upon the REGULAR baseline after the introduction of latent annotations.


Early question-answering (QA) systems, such as Baseball (Green et al., 1961) and Lunar (Woods, 1973) were carefully hand-crafted to answer questions in a limited domain, similar to the QA components of ELIZA (Weizenbaum, 1966) and SHRDLU (Winograd, 1972).

there has been a resurgence of QA systems following the TREC conferences with an emphasis on answering factoid questions.


A user querying a restaurant recommendation system expects more fine-grained information such as house specials, wine selections and choices on desserts rather than just general 'good food.'

to a 'text' summarization system, the textual space in a dialogue turn is often very limited.


Extraction of structured information from text is of interest to a large number of communities.

in the ads domain, the task has usually been simplified to that of classification or ranking.


That way a noun can appear in different vectors, hence in different clusters during hierarchical clustering as a result of its polysemy.

the underlying assumption is that a verb is monosemous with respect to its associated vector of nouns.


in the case of singer or need) as well as its ability to include synonyms from less frequent senses (e.g., the experiment sense of research or the verify sense of prove).

there are a number of ways it could be improved: Feature representations: Multiple prototypes improve Spearman correlation on WordSim-353 compared to previous methods using the same underlying representation (Agirre et al., 2009).


However, there are a number of ways it could be improved: Feature representations: Multiple prototypes improve Spearman correlation on WordSim-353 compared to previous methods using the same underlying representation (Agirre et al., 2009).

we have not yet evaluated its performance when using more powerful feature representations such those based on Latent or Explicit Semantic Analysis (Deerwester et al., 1990;Gabrilovich and Markovitch, 2007).


We posit that the finer-grained senses actually capture useful aspects of word meaning, leading to better correlation with WordSim-353.

it would be good to compare prototypes learned from supervised sense inventories to prototypes produced by automatic clustering.


In particular, they infer syntactic correspondences between the source and target languages through word alignment patterns, sometimes in combination with constraints from parser outputs.

word alignments are not perfect indicators of syntactic alignment, and syntactic systems are very sensitive to word alignment behavior.


In that case, the original article of the writer can be used as a feature for the classifier and the correct article, as judged by a native English speaker, will be viewed as the label.

obtaining annotated data for training is expensive and, since the native training data do not contain errors, we cannot use the writer's article as a feature for the classifier.


Moreover, training on error-tagged data is currently unrealistic in the majority of error correction scenarios, which suggests that using text with artificial mistakes is the only alternative to using clean data.

it has not been shown whether training on data with artificial errors is beneficial when compared to utilizing clean data.


Like blogs, conversations on Twitter occur in a public environment, where they can be collected for research purposes.

twitter posts are restricted to be no longer than 140 characters, which keeps interactions chat-like.


Note that τ is not a perfect measure of model quality for conversations; in some cases, multiple order-  ings of the same set of posts may form a perfectly acceptable conversation.

there are often strong constraints on the type of response we might expect to follow a particular dialogue act; for example, answers follow questions.


In the recent years, analysis of social media has attracted a lot of attention from the research community.

most of the work that uses social media focuses on blogs (Glance et al., 2004;Bansal and Koudas, 2007;Gruhl et al., 2005).


Different rows of Table 2 correspond to the following ways of ranking the threads: • Entropy + users -if the entropy of a thread is < 3.5, move to the back of the list, otherwise sort according to number of unique users Results show that ranking according to size of thread performs better than the baseline, and ranking according to the number of users is slightly better.

a sign test showed that neither of the two ranking strategies is significantly better than the baseline.


The baseline in this case is very strong and neither sorting according to the size of the thread nor according to the number of users outperforms the baseline.

adding the information about entropy significantly (p ≤ 0.01) improves the performance over all other ranking methods.


It was also discussed how this is related to finding decision boundaries through lowdensity regions of input distribution.

the obvious assumption here is that the classes are well separated and there in fact exists low-density regions between classes which can be treated as boundaries.


Previous research reported OOV detection accuracy on all test data.

once an OOV word has been observed in the training data for the OOV detector, even if it never appeared in the LVCSR training data, it is no longer truly OOV.


As a classification algorithm, Maximum Entropy assigns a label to each region independently.

ooV words tend to be recognized as two or more IV words, hence ooV regions tend to co-occur.


(Villavicencio et al., 2005) identify Machine Translation as an application of particular interest since " recognition of MWEs is necessary for systems to preserve the meaning and produce appropriate translations and avoid the generation of unnatural or nonsensical sentences in the target language."

statistical machine translation (SMT) typically does not model MWEs explicitly.


Meza-Ruiz and Riedel (2009) has shown that the predicate sense can improve the final SRL performance.

there is few discussion about the concrete influence of all word senses, i.e.


The facts of Sahaptin word order are also too complex for the customization system; in particular, it cannot model truly free word order (i.e., discontinuous noun phrases), and the attachment behavior of the second-position enclitic is similarly beyond its capability.

given these simplifying assumptions, the customization system is capable of modeling all the agreement and marking patterns of Sahaptin intransitive and transitive clauses shown in Tables 7 and 8 in R&R (1996, 676).


3 (1) Monolingual parsing is commonly thought of as a worst-case O(n 3 ) algorithm, even the known algorithms do have a grammar term that can contribute significantly.

since the grammar that a parser will employ is generally assumed to be fixed, Figure 2 plots the average runtime of the algorithm as a function of the Arabic sentence length on an Arabic-English phrasal ITG alignment task.


With the arrival of efficient, widecoverage parsers, it is feasible to create very large databases of trees.

existing approaches that use in-memory search, or relational or XML database technologies, do not scale up.


The length experiment here use a single term repeated multiple times.

there is a possibility that the results may vary when the terms are different, because it would involve additional time to load the term vectors of distinct elements into memory.


Unfortunately, it is known by previous results (Rambow and Satta, 1999) that it is not always possible to convert an LCFRS into such a binary form without increasing the fan-out.

we will show that it is always possible to build such a binarization for well-nested LCFRS.


In the Hallway Testing, the users were better trained and more familiar with IE tools (including the graphical interface of cross-document IE); and thus they can benefit more from the IE techniques.

in the Remote Evaluation, the users had quite diverse knowledge backgrounds.


(2009) who detect idiom types by using statistical methods that model the general idiomaticity of an expression and then combine this with a simple second-stage process that detects whether the target expression is used figuratively in a given context, based on whether the expression occurs in canonical form or not.

modeling token-based detection as a combination of type-based extraction and tokenbased classification has some drawbacks.


The answers constructed interactively were submitted to NIST as the final (post-interaction) run.

since these answers were significantly shorter than the initial run (given the short interaction period), the responses were "padded" by running additional iterations of automatic MMR until a length quota of 4000 characters had been achieved.


2), if sentence A contains all the information in sentence B but not vice versa, then B is also their intersection while A is their union and no sentence generation is required.

if the two sentences are too dissimilar, then no intersection is possible and the union is just the conjunction of the sentences.


Since topic changes sometimes happen within a single document, and our end task is sentence retrieval, we also investigate the notion of word cooccurrence in a smaller segment of text such as a sentence.

to the document-wise model, sentence-wise co-occurrence does not consider whole documents, and only concerns itself with the number of times that two words occur in the same sentence.


When a corpus is sense-tagged, mapping occurrences of a word to a concept is straightforward (since each sense of a word corresponds with a concept or synset in WordNet).

if the text has not been sense-tagged then all of the possible senses of a given word are incremented (as are their ancestors).


The Resnik (res) measure simply uses the Information Content of the LCS as the similarity value: The Resnik measure is considered somewhat coarse, since many different pairs of concepts may share the same LCS.

it is less likely to suffer from zero counts (and resulting undefined values) since in general the LCS of two concepts will not be a very specific concept (i.e., a leaf node in WordNet), but will instead be a somewhat more general concept that is more likely to have observed counts associated with it.


The one exception is with respect to the WS gold standard, where vector and lesk perform much better than the Information Content measures.

this seems reasonable since they are relatedness measures, and the WS corpus is annotated for relatedness rather than similarity.


To the machine learning community, treebanks are just collections of data, like pixels with captions, structural and behavioral facts about genes, or observations about wild boar populations.

to us computational linguists, treebanks are not naturally occurring data at all: they are the result of a very complex annotation process.


Linguistic phrase structure is most conveniently expressed in a phrase structure tree, while linguistic dependency is most conveniently expressed in a dependency tree.

we can express the same content in either type of tree!


Crowdsourcing is the use of the mass collaboration of Internet passers-by for large enterprises on the World Wide Web such as Wikipedia and survey companies.

a generalized way to monetize the many small tasks that make up a larger task is relatively new.


The performance of IdentiFinder is quite low on the IT business press.

a simple rule-based system was able to gain 10% improvement in precision with little recall sacrificed.


Hierarchical phrase-based translation (Hiero, (Chiang, 2005)) provides an attractive framework within which both short-and longdistance reorderings can be addressed consistently and ef ciently.

hiero is generally implemented with a constraint preventing the creation of rules with adjacent nonterminals, because such rules introduce computational and modeling challenges.


On the minus side, SWSD can only be useful for subjectivityambiguous words.

we showed (Su and Markert, 2008) that subjectivity-ambiguity is frequent (around 30% of common words).


They then incorporate these probabilities into machine translation.

they do not consider sentiment explicitly.


We tackle cross-lingual lexical substitution as a supervised task, using sets of manual translations for a target word as training data even for baseline system B.

we do not necessarily need dedicated human translated data as we could also use existing parallel texts in which the target word occurs.


That is, we posit that clickthrough information could provide important evidence for classifying query ambiguity.

we find that previously proposed clickthrough-based measures tend to conflate informational and ambiguous queries.


For instance, query "ako" was annotated as ambiguous, as it could refer to different popular websites, such as the site for Army Knowledge Online and the company site for A.K.O., Inc.

most users select the result for the Army Knowledge Online site, making the overall entropy low, resulting in prediction as a clear query.


Simplification can also be considered to be a form of MT in which the two "languages" in question are highly related.

note that ComplexEW and SimpleEW do not together constitute a clean parallel corpus, but rather an extremely noisy comparable corpus.


There are at least four possible edit operations: .

for this initial work we assume P (o 4 ) = 0.


In the field of machine translation, automatic metrics have proven quite valuable in system development for tracking progress and measuring the impact of incremental changes.

human judgment still plays a large role in the context of evaluating MT systems.


They simply choose the NPs in a product review as the product attribute candidates (Hu and Liu, 2004;Popescu and Etzioni, 2005;Yi et al., 2003).

this method limits the recall of the product attribute extraction for two reasons.


In particular, for a syntactic structure 3 T in the test set, if T exactly matches with one of the standard syntactic structures, then its corresponding string can be treated as a product attribute candidate.

this method fails to handle similar syntactic structures, such as the two structures in Figure 2.


This can illustrate that syntactic structures can cover more forms of the product attributes.

the recall of SynStru based method is not high, either.


At the moment, because of the small size of the data sets and the variety of writing styles in the development set, only tentative conclusions can be drawn.

even this small data set reveals clear problems for WSJ-trained parsers: the handling of long coordinated sentences (particularly in the presence of erratic punctuation usage), domain-specific fixed expressions and unknown words.


We also learn that new entity nominals are typically indefinite or have SBAR complements (captured by the CFG feature).

to nominals and pronouns, the choice of entity for a proper mention is governed more by entity frequency than antecedent distance.


On average the unconstrained model that contains more sentence pairs for rule extraction slightly outperforms the bounded condition which uses less data per epoch.

the static baseline and the bounded models both use the same number of sentence-pairs for TM training.


For presentation clarity we show only a sample of the full set of ten test points though all results follow the pattern that using more aligned sentences to derive our grammar set resulted in slightly better performance versus a restricted training set.

for the same coverage constraints not only do we achieve comparable performance to batch retrained models using the sOEM method of incremental adaptation, we are able to align and adopt new data from the input stream orders of magnitude quicker since we only align the mini-batch of sentences collected from the last epoch.


Clearly this question is not easy to address.

to get a rough idea we can look at the examples reported in Table 1.


The CN decoding has been shown to be efficient, just minimally larger than the single string decoding (Bertoldi et al., 2008).

in the current enhanced MT setting, the sequence of Steps 1 to 4 for building the CN from the noisy input text is quite costly.


Demographically related languages: Hindi and Kannada are languages spoken in the Indian subcontinent, though they are from different language families.

due to the shared culture and demographics, it is easy to create parallel names data between these two languages.


Task-accuracy results have generally been favorable.

it can be timeconsuming to apply Bayesian inference methods to each new problem.


Traditional machine learning algorithms are typically designed for a single machine, and designing an efficient training mechanism for analogous algorithms on a computing clusteroften via a map-reduce framework (Dean and Ghemawat, 2004) -is an active area of research (Chu et al., 2007).

unlike many batch learning algorithms that can easily be distributed through the gradient calculation, a distributed training analog for the perceptron is less clear cut.


This counter example does not say that a parameter mixing strategy will not converge.

if T is separable, then each of its subsets is separable and converge via Theorem 1.


For example, it seems reasonable to assume that "brooklyn pizza" and "pizza brooklyn" denote roughly the same user intent.

the pair has an edit distance of two (delete-insert), while the distance between "brooklyn pizza" and the less relevant "brooklyn college" is only one (substitute).


Some researchers combine features by manual rules or weights.

it is not convenient to directly use these rules or weights in another data set.


Machine Transliteration has been studied extensively in the context of Machine Translation and Cross-Language Information Retrieval (Knight and Graehl, 1998), (Virga and Khudanpur, 2003), (Kuo et al., 2006), (Sherif and Kondrak, 2007), (Ravi and Knight, 2009), (Li et al., 2009), (Khapra and Bhattacharyya, 2009).

machine Transliteration followed by string similarity search gives less-thansatisfactory solution for the cross-language name search problem as we will see later in Section 4.


Intuitively, there should be a relatively small prior probability of introducing a new word-object pair, corresponding to a small α 1 value.

most other words don't refer to the topic object (or any other object for that matter), corresponding to a much larger α 0 value.


A small concentration parameter biases the estimator to prefer a small set of word types.

the relatively large concentration parameter for the nonreferring words tends to result in most of the words receiving highest probability as non-referring words.


A segmenting model without meanings cannot share the word learner's reluctance to propose new meaning-bearing word types and might propose three separate types for "your book", "a book", and "the book".

with a small enough prior on new referring word types, the word learner that discovers a common referent for all three sequences and, preferring fewer referring word types, is more likely to discover the common subsequence "book".


It is expected that a label predicated on requests for action should rely on the isolation of verb stems, but this is still a very substantial gain.

to this 391.2% gain in accuracy for Chichewa, the gain for Indep = language independent, Chich = specific to Chichewa, ( ) = not significant (ρ > 0.05, χ 2 ), Final = Gain of the 'Morph-Config, Indep' model over the Baseline.


Unfortunately, word-based techniques face problems due to data sparsity: not all words in the test set are seen during training.

consonant-based approaches rarely face the analogous problem of previously unseen consonants.


(2006), we use a hybrid word-and consonant-level approach based on the following observations (statistics taken from the Syriac training and development sets explained in Section 4): Contrary to observations 1 and 2, consonant-level approaches dedicate modeling capacity to an exponential (in the number of consonants) number of possible diacritizations of a word.

a word-level approach directly models the (few) diacritized forms seen in training.


In sentences containing no rare words, the well-known Viterbi algorithm can be used to find the optimum.

as can be seen in Figure 1b, predictions in the consonant-level model (e.g., C 5,1...4 ) depend on previously diacritized words (D 4 ), and some diacritized words (e.g., D 6 ) depend on diacritics in the previous rare word (C 5,1...4 ).


Thus, the concept of space is only learnt later on when the person learns how to use a computer.

space is introduced as a tool to control the correct letter shaping and not to consistently separate words.


As the examples suggest, the reduplication may not only be limited to word initial position and may also occur word medially.

if the length of base word is less than four, it is further to avoid function words (case markers, postpositions, aux-iliaries, etc.)


As has been discussed, space does not necessarily indicate word boundary.

presence of space does imply word or morpheme boundary in many 6 The word ٰ اعلی is written with the super-script Alef placed on Lam and Yay characters.


Since the alignment in the HMM-based model is determined by a hidden variable, the EM algorithm is required to estimate the parameters of the model (see (Och and Ney, 2003)).

the standard EM algorithm is not appropriate to incrementally extend our HMM-based models because it is designed to work in batch training scenarios.


This prevents models from doing better or worse just because they received different starting points.

it is still possible that certain random starting points are better for some evaluation metrics than others.


Phrases were extracted using the grow heuristic (Koehn et al., 2003).

we threw away all phrases that have a P (e|f ) < 0.0001 in order to reduce the size of the phrase table.


This maybe due to the monotone na-ture of the reference translations and the fact that having multiple references reduces the need for reorderings.

it is possible that differences between training to WER and TER would become more apparent using models that allow for longer distance reorderings or that do a better job of capturing what reorderings are acceptable.


The Arabic results also trend toward suggesting that BLEU:4 is better than either standard METEOR and METEOR α 0.5.

for the Chinese models, training to standard METEOR and METEOR α 0.5 is about as good as training to BLEU:4.


Edit distance models tend to do poorly when evaluated on other metrics, as do models trained using METEOR.

training models to METEOR can be made more robust by setting α to 0.5, which balances the importance the metric assigns to precision and recall.


3 Our algorithm requires running the IO algorithm for each yield in the variational distribution, for each nonterminal, and for each sentence.

iO runs with much smaller grammars coming from the grammatons.


At first glance it seems that variational inference is slower than MCMC sampling.

note that the cost of the grammar preprocessing step is amortized over all experiments with the specific grammar, and the E-step with variational inference can be parallelized, while sampling requires an update of a global set of parameters after each tree update.


For example, the sentence-based sampler samples all the variables associated with a sentence at once (e.g., the entire tag sequence).

this blocking does not deal with the strong type-based coupling (e.g., all instances of a word should be tagged similarly).


Figure 3 shows examples of same-type sites for our three models.

even if all sites in S have the same type, we still cannot sample b S jointly, since changing one b s might change the type of another site s ; indeed, this dependence is reflected in (5), which shows that types depend on z.


Skip Approximation Large type blocks mean larger moves.

such a block S is also sampled more frequently-once for every choice of a pivot site s 0 ∈ S. we found that empirically, b S changes very infrequently.


All of these methods maintain distributions over (or settings of) the latent variables of the model and update the representation iteratively (see Gao and Johnson (2008) for an overview in the context of POS induction).

these methods are at the core all token-based, since they only update variables in a single example at a time.


If the paraphrase P passes the current test, in the next iteration it will be tested by taking one more context word into account, namely W 2 3 .

if the paraphrase P fails the current (n, C) check the checking procedure will terminate and report that the paraphrase fails.


However, If the paraphrase P fails the current (n, C) check the checking procedure will terminate and report that the paraphrase fails.

if the paraphrase passes all the (n, C) checks where C = 1 to maxC, the procedure determines the paraphrase as acceptable.


If an information unit contains at least one paraphrasable sentence, this information unit implies the embedding of 1.

if none of the sentences in the information unit are paraphrasable, it implies the embedding of 0.


For example, if {a,b} occurs eight times in the test corpus as <a,b> and two times as <b,a>, we will be limited to a maximum accuracy of 80% (presuming our system correctly predicts the more common ordering).

even though suggesting <b,a> is not strictly incorrect, we generally prefer to reward a system that produces more common orderings, an attribute not emphasized by type-based metrics.


We predict pairwise orderings with 88% accuracy, so we would expect no worse than (.88) 3 , or 68% accuracy on such sequences.

the pairwise accuracy declines on longer NPs, so it underperforms even that theoretical minimum.


We take a rule-based approach in order to leverage this linguistic knowledge.

since many phenomena pertaining to question generation are not so easily encoded with rules, we include statistical ranking as an integral component.


This would allow, for example, the addition of a rule to generate why questions that builds off of the existing rules for subject-auxiliary inversion, verb decomposition, etc.

previous QG approaches have employed separate rules for specific sentence types (e.g., Mitkov and Ha, 2003;Gates, 2008).


In Yarowsky's work, his system requires an initial, manually-supplied collocation as a feature for each sense of a keyword.

we can use GLOSSY's extracted glosses to supply starter features fully automatically, using only an unlabeled corpus.


If we could predict subject and complements of the word well, supertagging would be an easier job to do.

current widely used sequence labeling models have the limited ability to catch these longdistance syntactic relations.


Since the baseline parser is different, we didn't make a direct comparison here.

it would be interesting to compare these two different ways of incorporating the dependency parser into HPSG parsing.


The only approach that outperforms the LAS score of the unweighted voting model is the model that weighs parsers by their accuracy for a given modifier POS tag, but the improvement is marginal.

the number of base parsers in the ensemble pool is crucial: performance generally continues to improve as more base parsers are considered.


Unlike voting, a metaclassifier can combine evidence from multiple contexts (such as the ones listed in Table 2).

in our experiments such a meta-classifier 4 did not offer any gains over the much simpler unweighted voting strategy.


This performance drop is statistically significant out of domain.

the difference between the Eisner and Attardi algorithms is not statistically significant out of domain.


Recent work has shown that the combination of base parsers at learning time, e.g., through stacking, yields considerable benefits (Nivre and McDonald, 2008;Attardi and Dell'Orletta, 2009).

it is unclear how these approaches compare against the simpler ensemble models, which combine parsers only at runtime.


SITGs have proven to be a powerful tool in Syntax Machine Translation.

the algorithms have been proposed do not explore all the possible parse trees.


Using the reranker features distributed with the Brown reranker (Charniak and Johnson, 2005), which we call the "standard" set below, we obtained no overall improvement in f-score when either reranking the Berkeley parser n-best lists alone, or when the Berkeley parses were combined with the Brown parses.

it is possible that these results reflect the fact that the features used by the reranker were chosen because they improve the Brown parser, i.e., they are the result of feature selection based on reranking the Brown parser's n-best lists.


It would lead to deemphasizing those unusual types of information that might be being discussed as part of a post.

one might expect that those things that are unusual types of information might actually be more likely to be the in-focus information within an initiation that responses may be likely to refer to.


Conversational Agents have been shown to be effective tutors in a wide range of educational domains.

these agents are often ignored and abused in collaborative learning scenarios involving multiple students.


In all three conditions, students go through the same task plan.

the degree of social performance is varied from minimal (Task) to ideal (Human).


On the right side of Table 2, we notice that the human tutors (H) were rated higher on being part of the team (Integration), being more liked, being friendlier and keeping the group more socially comfortable (T.Releasing).

the social tutors (S) were rated to be friendlier and were only marginally better at being seen as part of the team.


Language identification has traditionally been approached with character-level language models.

the language model approach crucially depends on the length of the text in question.


Both approaches benefit from their ability to consider large, flexible spans of source context when making transduction decisions.

they encode this context in different ways, providing their respective models with different information.


One approach to string transduction is to view it as a tagging problem where the input characters are tagged with the output characters.

since sounds are often represented by multicharacter units, the relationship between the input and output characters is often complex.


In the first 2 passes, we use a standard bi-gram LM to generate lattices, followed by a standard tri-gram LM rescoring of lattices.

in the third pass, we generate both lattices and N-best lists based on the same bi-gram LM.


provement of 0.8% (about 4.8% relative) compared to the standard tri-gram lattice rescoring.

we have 0.6% absolute improvement (about 3.7% relative) compared to the standard 4gram lattice rescoring.


(Zitouni et al., 2005) used Arabic morphologically segmented data and claimed to have very competitive results in ACE 2003 and ACE 2004 data.

(Benajiba et al., 2008) report good results for Arabic NER on ACE 2003, 2004and 2005 segmentation.


This is because the token itself becomes insignificant information to the classifier.

when only punctuation separation is performed (W ord s ), the data is significantly sparse and the obtained results achieves high F-measure (77.1) only when outputs of other classifiers are used.


Hence, we keep the edge to indicate that the child can be used as a preferred substitute for the parent.

the edge is removed if the ratio is small (less than a threshold t, see Fig.


Using aggressive pruning, the list size and number of redun-dant phrase patterns are greatly reduced.

the classification accuracy does not decrease.


The detection of pitch accents and phrase boundaries has received significantly more research attention than the classification of accent types and phrase ending behavior.

one technique that has been used in a number of research efforts is to simultaneously detect and classify pitch accent.


In both systems, the frame-based log posterior vector of P (phone|acoustics) over all phones is decorrelated using the Karhunen-Loeve (KL) transform; unlike MLPs, CRFs take into account the entire label sequence when computing local posteriors.

posterior estimates from the CRF tend to be overconfident compared to MLP posteriors (Morris and Fosler-Lussier, 2009).


Calculating these is computationally inexpensive for many simple tasks (such as classification and regression).

marginal and MAP inference tends to be expensive for complex structured prediction models (such as the joint information extraction models of ), making semisupervised learning intractable.


The softmax-margin approach offers (1) a convex objective, (2) the ability to incorporate task-specific cost functions, and (3) a probabilistic interpretation (which supports, e.g., hidden-variable learning and computation of posteriors).

max-margin training and MIRA do not provide (3); risk and JRB do not provide (1); and CLL does not support (2).


A drawback of this approach is that it is extremely local: while decisions can be based on complex structures on the left, they can look only at a few words to the right.

our algorithm builds a dependency tree by iteratively selecting the best pair of neighbours to connect at each parsing step.


Each performed action changes the partial struc-tures and with it the extracted features and the computed scores.

these changes are limited to a fixed local context around the attachment point of the action.


Shorter edges are arguably easier to predict, and our parses builds them early in time.

it is also capable of producing long dependencies at later stages in the parsing process.


(2009b) study dependency treebanks for nine languages and find that all dependency structures meet the mildly ill-nested condition in the dependency treebanks for some gap degree.

they do not report the maximum gap degree or parsing complexity.


This is certainly true when the graph is of high quality and all paths trustworthy.

in a graph constructed from web cooccurrence statistics, this is rarely the case.


Ideally we would see an order on such phrases, e.g., "more brittle" has a larger negative polarity than "brittle", which in turn has a larger negative polarity than "less brittle".

this is rarely the case and usually the adjective has the highest polarity magnitude.


In the approach, a subjective sentence is represented as a set of words in the sentence, ignoring word order and head-modifier relation between words.

sentiment classification is different from traditional topic-based text classification.


Recently, several methods have been proposed to cope with the problem (Zaenen, 2004;Ikeda et al., 2008).

these methods are based on flat bag-of-features representation, and do not consider syntactic structures which seem essential to infer the polarity of a whole sentence.


Titov and McDonald (2008b) underline the need for unsupervised methods for aspect detection.

according to the authors, existing topic models, such as standard Latent Dirichlet Allocation (LDA) (Blei et al., 2003), are not suited to the task of aspect detection in reviews, because they tend to capture global topics in the data, rather than rateable aspects pertinent to the review.


For example, for many verbs, nsubj tends to start a cause path and dobj a theme path.

for "bind" that signifies a Binding event, both lead to themes, as in "A binds B".


Several studies have been performed on identifying PICO elements in abstracts (Demner-Fushman and Lin, 2007;Hansen et al., 2008;Chung, 2009).

all of them are reporting coarsegrain (sentence-level) tagging methods that have not yet been shown to be sufficient for the purpose of IR.


Moreover, there is currently no standard test collection of questions in PICO structure available for evaluation.

the most critical aspect in IR is term weighting.


These approaches follow the assumption that the user knows where the most relevant information is located.

(Kamps et al., 2005) showed that it is preferable to use structure as a search hint, and not as a strict search requirement The second approach consists in integrating the document structure at the indexing step by introducing a structure weighting scheme (Wilkinson, 1994).


There has been several studies that cover the PICO extraction problem.

as far as we know, none of them analyses and uses the positional ditribution of these elements within the documents for the purpose of IR.


It could also be caused by the limited number of query in our test collection.

we can determine reasonable weights by tuning each part weight separately.


A straightforward idea is to detect PICO elements in documents and use the elements in the retrieval process.

this approach does not work well because of the difficulty to arrive at a consistent tagging of these elements.


Given a query, search engines retrieve relevant pictures by analyzing the image caption (if it exists), textual descriptions found adjacent to the image, and other text-related factors such as the file name of the image.

since they do not analyze the actual content of the images, search engines cannot be used to retrieve pictures from unannotated collections.


The probability of a document d in a corpus is defined as: Computing the posterior distribution P(θ, z|d, α, β) of the hidden variables given a document is intractable in general.

a variety of approximate inference algorithms have been proposed in the literature including variational inference which our model adopts .


All are almost surprisingly intuitive, but this is not terribly surprising since Chinese and English have very similar large-scale structures (both are head initial, both have adjectives and quantifiers that precede nouns).

we see two entries in the list (starred) that correspond to an En- glish word order that is ungrammatical in Chinese: PP modifiers in Chinese typically precede the VPs they modify, and CPs (relative clauses) also typically precede the nouns they modify.


4aThe VSO basic word order is evident: early in the sentence, there is a strong tendency towards right movement around arguments after covering the verb.

right movement is increasingly penalized at the end of the sentence.


The binarization in Figure 1 is unfortunate because the obtained production cannot be factorized such that only two nonterminals occur in each rule.

the binarization of Figure 4 allows the factorization into S(U , NP ) ↔ S(U , NP ) and U : @(NP , V ) ↔ @(V , NP ), which are fully binarized productions.


However, the binarization of Figure 4 allows the factorization into S(U , NP ) ↔ S(U , NP ) and U : @(NP , V ) ↔ @(V , NP ), which are fully binarized productions.

in general, STSGs (or SCFGs or extended tree transducers) cannot be fully binarized as shown in Aho and Ullman (1972).


Consequently, we cannot compute forward or backward applications for arbitrary MBOT.

if the MBOT is equivalent to an STSG (for example, because it was constructed by the method presented before Theorem 3), then forward and backward application can be computed essentially as for STSG.


MBOT can efficiently be used (with computational benefits) as an alternative representation for transformations computed by STSG (or compositions of STSG).

mBOT can also compute transformations, of which the domain or range cannot be represented by a TSG.


Both methods were trained and tested on data from the Ziff-Davis corpus (Knight and Marcu, 2002), and they achieved very similar grammaticality and meaning preservation scores, with no statistically significant difference.

their compression rates (counted in words) were very different: 70.37% for the noisy-channel method and 57.19% for the C4.5-based one.


Our method is similar to Nomoto's, in that it uses two stages, one that chops the source dependency tree generating candidate compressions, and one that ranks the candidates.

we experimented with more elaborate ranking models, and our method does not employ any manually crafted rules.


guage model trained on a large background corpus.

language models tend to assign smaller probabilities to longer sentences; therefore they favor short sentences, but not necessarily the most appropriate compressions.


Extrinsic evaluations have also shown that, while extractive summaries may be less coherent than human abstracts, users still find them to be valuable tools for browsing documents (He et al., 1999;.

these same evaluations also indicate that concise abstracts are generally preferred by users and lead to higher objective task scores.


The system described thus far may appear extractive in nature, as the transformation step is identifying informative sentences in the conversation.

these selected sentences correspond to < participant, relation, entity > triples in the ontology, for which we can subsequently generate novel text by creating linguistic annotations of the conversation ontology (Galanis and Androutsopolous, 2007).


This is due to the fact that the leading sentences for these two domains do not indicate any significance, hence the Lead system just behaves like Random.

for the scientific and newswire domains, the leading sentences do have  importance so the Lead system consistently outperforms Random.


f MMR , is not guaranteed everywhere monotone.

our theoretical results still holds for f MMR with high probability in practice.


We assume that there is not enough high-quality data to build a monolingual selectional preference model for the source language (shown by dotted lines).

we can use a bilingual vector space, that is, a semantic space in which words of both the source and the target language are represented, to translate each source language word s into the target language by identifying its nearest (most similar) target word tr(s): Now we can use a target language selectional preference model to obtain plausibilities for source triples: where the superscript indicates the language.


Data Our experiments were carried out on the CoNLL 2008 (Surdeanu et al., 2008) training dataset which contains both verbal and nominal predicates.

we focused solely on verbal predicates, following most previous work on semantic role labeling (Màrquez et al., 2008).


Since different similarity functions can be used within this framework, one may wish to select the one that is the most appropriate or relevant to the task considered.

a crucial requirement for this choice to be realistic is to ensure that for the family of similarity functions considered the expected similarity maximization is efficiently computable.


As mentioned earlier, we leave this question to future work.

we can offer a brief look at how one could tackle this question.


The empirical adequacy of 2-SCFG models would presumably be lower with automatically-aligned texts and if the study also included non-European languages.

phrase-based systems can properly handle inside-out alignments when used with a reasonably large distortion limit, and all configurations in Fig.


6 Likewise, the procedure could be applied to statistical systems that only generate k-best lists.

we would not expect the same strong performance from model combination in these constrained settings.


Additionally, the experiments were performed over a large corpus of messages that are not available for use by other researchers.

we use messages from the widely-available Enron email corpus (Klimt and Yang, 2004) for our own experiments.


This strongly suggests that without zoning, the classifier is not learning features from the training set at a useful level of generality.

once we add the zoning classifier, the top-10 unigrams and bigrams appear to correspond much better with linguistic intuitions about the language of requests.


There is much disagreement about the units and elementary relations of discourse structure, but they agree that the structures are hierarchical, most commonly trees (Marcu, 2000), while others have argued for directed acyclic graphs (Danlos, 2004), or general graphs (Wolf and Gibson, 2004).

most of the segmentation research to date has focused on linear segmentation, in which segments are non-overlapping and sequential, and it has been argued that this sequence model is sufficient for many purposes (Hearst, 1994).


The mean scores for the BIN baseline are over 50% on the encyclopedia data.

the mean score for BIN on the Choi standard data (Fig.


For example, SkillSum (Williams and Reiter, 2008) and ICONOCLAST (Power et al., 2003) are two contemporary generation systems that allow for specifying aspects of style such as choice of discourse marker, clause order, repetition and sentence and paragraph lengths in the form of constraints that can be optimised.

to date, these systems do not consider syntactic reformulations of the type we are interested in.


This pulls the average for reformulated sentences down.

on average 2 out of 7 reformulations score quite high.


In general, one would expect that short transformation sequences to provide good evidence of true entailments.

to account for the grandparent-child relationship in the hypothesis, TED would produce a fairly long sequence, relabeling nearby to be near, deleting the two nodes for Rossville Blvd, and then reinserting those nodes under near.


Settings of 0.1, 0.2, 0.3, and 0.4 led to 10-fold cross-validation The main difference between our kernel and the CTK is that we sum over all pairs of subtrees (Equation 3).

the CTK only considers only one pair of subtrees.


9 In accuracy values that were not significantly different from each other.

we did observe that increased search failure ( §3.4) resulted from settings above 0.5.


Chawathe and Garcia-Molina (1997) describe a tree edit algorithm for detecting changes in structured documents that incorporates edits for moving subtrees and reordering children.

they make assumptions unsuitable for natural language, such as the absence of re-cursive syntactic rewrite rules.


Lexical-syntactic rules can be automatically extracted from plain corpora (e.g., (Lin and Pantel, 2001;Szpektor and Dagan, 2008)) but the quality (also in terms of little noise) and the coverage is low.

rules written at the semantic level are more accurate but their automatic design is difficult and so they are typically hand-coded for the specific phenomena.


by using FrameNet semantics (e.g., like in (Burchardt et al., 2007)), it is possible to encode a lexical-syntactic rule using the KILLING and the DEATH frames, i.e.

: to use this model, specific rules and a semantic role labeler on the specific corpora are needed.


Again, the syntactic rules (with variables) which this kernel BNC WN WIKI RTE2 0.55 0.42 0.83 RTE3 0.54 0.41 0.83 RTE5 0.45 0.34 0.82 Table 3: Coverage of the different resources for the words of the three datasets can provide are not enough general for RTE3.

maxSSTK+WOK improves WOK on all datasets thanks to its generalization ability.


This may be due to the fact  that, as shown in Table 1, the overall differences between partners in mixed-gender pairs are quite low, and so neither partner may be doing much turn-byturn matching.

as we expected, entrainment is least prevalent among male-male pairs.


This reflects the intuition that someone overly eager to be liked may be perceived as annoying and socially inept.

similarity-attraction theory states that similarity promotes attraction, and someone might therefore entrain in order to obtain his partner's social approval.


As we expected, giving encouragement is correlated with entrainment for all three gender groups, and trying to be liked is correlated with entrainment for male-male and female-male groups.

trying to dominate is not correlated with entrainment on any feature, and conversation awkward is actually positively correlated with entrainment on jitter.


A high number is negative in that it is the sign of an inefficient dialogue, one which takes many turn exchanges to accomplish the objective.

it may also be the sign of easy, flowing dialogue between the partners.


Besides shell language, there were other annotations relevant to essay scoring.

we ignored them for this study because they are not directly relevant to the task of shell language detection.


For the annotated essay test set ( §3.2), the percentage of tokens tagged as shell was 14.0% (11.6% were labeled as shell by the first annotator).

the percentage of tokens tagged as shell was 4.2% for Lincoln-Douglas, 5.4% for Kennedy-Nixon, 4.6% for Gore-Bush, and 4.8% for Obama-McCain.


It is not completely clear whether the smaller percentages tagged as shell are due to a lack of coverage by the shell detector or more substantial differences in the domain.

it seems that these debates genuinely include less shell.


Also, Bush employs a somewhat atypical sentence structure here: "It's not what I think and its not my intentions and not my plan."

the system also incorrectly tagged sequences as shell, particularly in short sentences (e.g., "Are we as strong as we should be?").


Discourse markers, however, are typically only single words or short phrases that express a limited number of relationships.

shell can capture longer sequences that express more complex relationships between the components of an argumentative discourse (e.g., "But let's get back to the core issue here" signals that the following point is more important than the previous one).


In shell detection, we focus on the lexico-syntactic level, aiming to identify the bold words as shell.

work on argumentation schemes focuses at a higher level of abstraction, aiming to classify the sentence as an attempt to persuade by appealing to an external authority.


There have been many attempts over the last decade to develop model-based approaches to the phrase alignment problem (Marcu and Wong, 2002;Birch et al., 2006;DeNero et al., 2008;Blunsom et al., 2009).

most of these have met with limited success compared to the simpler heuristic method.


e 3 e 4 e 5 e 6 e 7 Because there is a sure link at a 48 , σ f 8 = [4, 4] does not include the possible link at a 38 .

f 7 only has possible links, so σ f 7 = [5, 6] is the span containing those.


DeNero and Klein 2010implicitly included these constraints in their representation: instead of sets of variables, they used a structured representation that only encodes triples (a, π, σ) satisfying both the mapping π = π(a) and the structural constraint that a can be generated by a block ITG grammar.

our inference procedure, BP, requires that we represent (a, π, σ) as an assignment of values to a set of variables.


The BP ITG model performs comparably to the Viterbi ITG model.

because posterior decoding permits explicit tradeoffs between precision and recall, it can do much better in the recallbiased measures, even though the Viterbi ITG model was explicitly trained to maximize F 5 (DeNero and   Klein, 2010).


One practical solution is to restrict the output vocabulary to a short-list composed of the most frequent words (Schwenk, 2007).

the usual size of the short-list is under 20k, which does not seem sufficient to faithfully represent the translation models of section 2.


One solution would be full-blown transliteration (Knight and Graehl, 1998), followed by application of Jaro-Winkler.

transliteration systems are complex and require significant training resources.


11 Because a similar corpus did not exist for development, we split the evaluation corpus into development and test sections.

the usual method of splitting by document would not confine all mentions of each entity to one side of the split.


First, the model learns that high edit distance is predictive of a mismatch.

singleton strings that do not match often have a lower edit distance than longer strings that do match.


To our knowledge, Baron and Freedman (2008) reported the only previous results on the ACE2008 data set.

they only gave gold results for English, and clustered the entire evaluation corpus (test+development).


Within this work, specific affect definitions vary slightly with the intention of being coherent within the application and domain and being relevant to the specific adaptation goal (Martalo et al., 2008).

affective systems researchers generally agree that disengaged users show little involvement in the interaction, and often display facial, gestural and linguistic signals such as gaze avoidance, finger tapping, humming, sarcasm, et cetera.


All turns are used in the disengagement detection experiments described next.

only the training problem dialogues (360, 5 per student, 6044 student turns) are used for the performance analyses in Sections 6-7, because the final test problem was given after the instruments measuring performance (survey and posttest).


Compared to the results in Table 2, we find that the VSM and LSA methods are very robust to recognition errors, and we only observe slight correlation decreases on these features.

the decrease for the PMI-based method is quite large.


Our experimental results showed that all the features obtained good correlations with human proficiency scores if there are no recognition errors in the text transcripts, with the PMI-based method performing the best over three similarity measures.

if we used ASR transcripts, we observed a marked performance drop for the PMI-based method.


In our experiments, we noticed that shorter conversations suffer from poor classification.

the results from the above section appear to contradict this assertion, as a 30-word window can give very good performance.


Though the performance gain over the random ranker has shrunk considerably, there is still some utility in using the opening of a conversation to determine its ultimate duration.

it is clear predicting duration via conversation opening is a much more difficult task overall.


show that on certain synthetic-data problems, this frequentist training regimen significantly reduced test-data loss compared to approximate maximum likelihood estimation (MLE).

this method has not been evaluated on real-world problems until now.


Finally, we hypothesized that sum-product inference may produce more accurate results in certain cases as it allows more information about different parts of the model to be exchanged.

our results show that for these three problems, sum-product and max-product inference yield statistically indistinguishable results.


These computations are tractable for HMMs, since the distribution q(t) = p θ (t | w) that is optimal at the E-step (which makes the inequality tight) can be represented as a lattice (a certain kind of weighted DFA), and this makes the M-step tractable via the forward-backward algorithm.

there are many extensions such as factorial HMMs and Bayesian HMMs in which an expectation under p θ (t | w) involves an intractable sum.


The other view is that the corpus loglikelihood is a sum over many terms of the form (2), one for each training sentence w, and we bound each summand individually using a different q φ .

neither view leads to a practical implementation in our setting.


This is because this corpus is not much bigger than the 5-gram language model built from it (at our current pruning level), and so the overhead of the more complex n-gram EM method is a net disadvantage.

when moving to larger corpora, the iterations of n-gram EM become as fast as standard EM and then faster.


This method works significantly better than standard perceptron, and is followed by later incremental parsers, for instance in (Zhang and Clark, 2008;Huang and Sagae, 2010).

two problems remain: first, up till now there has been no theoretical justification for early update; and secondly, it makes learning extremely slow as witnessed by the above-cited papers because it only learns on partial examples and often requires 15-40 iterations to converge while normal perceptron converges in 5-10 iterations (Collins, 2002).


A triple (x, y, z) is said to be a violation in training scenario S = D, Φ, C with respect to weight vector w if (x, y, z) ∈ C and w • ∆Φ(x, y, z) ≤ 0.

intuitively, this means model w is possible to mislabel example x (though not necessarily to z) since y is not its single highest scoring label under w. z ∈ Y(x) and z = y, so This lemma basically says exact search guarantees violation in each update, but as we will see in the convergence proof, violation itself is more fundamental than search exactness.


And human-assisted metrics (Snover et al., 2006) have enabled and supported large-scale U.S. government sponsored programs, such as DARPA GALE (Olive et al., 2011).

these metrics have started to show signs of wear and tear.


In CV annotation it is typical to remove infrequent terms from both the keyword vocabulary and the evaluation data because CV algorithms typically need a large number of examples to train on.

using NLP systems and baselines one can correctly annotate using keywords that did not appear in the training set.


It also fails to capture variation in how humans describe images, since it is limited to one caption per image.

3 captions are a cheap source of data; BBC has ten times as many images as UNT.


In the first part of our paper, we present classification experiments with newer MT metrics not available in 2005, a worthwhile exercise in itself.

we go much further in our study: • We apply our approach to two different paraphrase datasets (MSRP and PAN) that were created via different processes.


Similarly, to indicate locations, a preposition is normally required in English (e.g., 'on the hill').

in Classical Chinese, the preposition is frequently omitted, with the bare locative noun phrase modifying the verb directly.


Moreover, this approach allows for easy addition of new exercises : as long as an exercise relies on the concepts covered by the domain model, the system can apply standard instructional strategies to each new question automatically.

this approach is significantly limited by the requirement that the domain be small enough to allow comprehensive knowledge engineering, and it is very labor-intensive even for small domains.


For example, a likely response to "partially correct incomplete" would be to tell the student that what they said so far was correct but it had some gaps, and to encourage them to fill in those gaps.

the response to "contradictory" would emphasize that there is a mistake and the student needs to change their answer rather than just expand it.


This baseline is based on the lexical overlap baseline used in RTE tasks (Bentivogli et al., 2009).

we measured overlap with the question text in addition to the overlap with the expected answers.


Future systems developed for this task can benefit from the large amount of existing work on recognizing textual entailment (Giampiccolo et al., 2007;Giampiccolo et al., 2008;Bentivogli et al., 2009) and on detecting contradiction (Ritter et al., 2008;De Marneffe et al., 2008).

there are substantial challenges in applying the RTE tools directly to this data set.


's algorithm is as a form of structured ramp loss.

another interpretation is given by McAllester et al.


(depending on whether a cycle after the initial aaa has positive or negative weight), but never the optimal aaaaa.

if G instead encoded 5-grams, this would not be a problem because a path through a 5-gram machine could accept aaaaa without traversing a cycle.


Setting smaller batch size implies frequent updates to the parameters and a faster convergence.


This starting output tree is typically the best parse of the string that we want to translate.

instead of a single tree, we want to use all parses of this sentence together with their parse scores.


Applied to a corpus of journalistic articles, CATIT was able to provide headings both informative and catchy.

syntactical patterns used for titles building were short (two terms) and experience showed that longer titles were often preferred.


Finally, the annotation also includes the appropriate usage rule from the set in Table 1.

3 the NUCLE and HOO data sets do not have this granularity of information (the annotation only indicates whether a comma should be inserted or removed) and are not exhaustively annotated.


The fact that the Chinese determiner is not necessarily a maximal projection of the noun -in other words, the determiner does not 'close off' a level of NPalso argues against importing the English analysis.

the English CCGbank determiner category NP/N reflects the fact that determiners 'close off' NP -further modification by noun modifiers is blocked after combining with a determiner.


The well-known noun/verb ambiguity in Chinese (where, e.g., 设计建设 'design-build' is both a verbal compound 'design and build' and a noun compound 'design and construction') greatly affects parsing accuracy (Levy and Manning, 2003).

little work has quantified the impact of noun/verb ambiguity on parsing, and for that matter, the impact of other frequent confusion types.


This is expected; Chinese CCGbank does not distinguish between noun modifiers (NN) and adjectives (JJ).

the critical noun/verb ambiguity, and the confusion between DEC/DEG (two senses of the particle 的 de) adversely impact F -score.


The creation of the dependency representation is similar in basic aspects to many other approaches, in that we utilize some basic assumptions about head relations to decompose the full tree into smaller units.

we first decompose the original trees into a Tree Insertion Grammar representation (Chiang, 2003), utilizing tree substitution and sister adjunction.


In this way, the dependency representation in Figure 3 follows immediately from Figure 2.

in addition, we utilize the TIG derivation tree and the structures of the elementary trees to create a supertag (in the sense discussed in Section 1) for each word.


100 is the head of the phrase $ 100 *U* in the PTB PS (a), as shown in the dependency structure (b).

because it only has one child in addition to the $, no additional QP node is created in the phrase structure representation in (c).


Our interpretation of this is that it provides an indication of what the parser is providing on top of the gold dependency structure, which is roughly the same information that we have encoded in our DS to PS code.

because the Wang and Zong (2010) system performs better than our USE-POS version, it is likely learning some of the nonstraightforward cases of how USE-POS tags can bootstrap the syntactic structure that our USE-POS version is missing.


6 We can see that using paraphrases improves the results over the unsupervised state of the art, regardless of which source of paraphrasing is used.

it is clear that not all types of paraphrases are equally helpful.


We can see that the results are much more stable with respect to recall -there is an initial drop in performance when we remove the first 10% of paraphrases, but after that removing more paraphrases does not affect performance very much.

changing the precision has a bigger impact on the results.


More and more language workers and learners use the MT systems on the Web for information gathering and language learning.

web translation systems typically offer top-1 translations (which are usually far from perfect) and hardly interact with the user.


As expected, the predicted ASR accuracy increases as EEG classification accuracy increases, for both groups (adults and children) and both levels of difficulty (easy and difficult).

figure  1a and 1b shows that WACC was much lower for children than for adults, especially on difficult utterances, where even 100% simulated EEG classifier accuracy achieves barely 20% WACC.


The fact that special characters and numbers behave similarly across languages is encouraging as one would expect less crosslinguistic variation for these two classes of words.

"true" words (those exclusively composed of alphabetic characters) show more variation from language to language: 0.03 ≤ ∆ W ≤ 0.12.


Most of the research on both G2P and MTL assumes the existence of a homogeneous training set of input-output pairs.

following the pivot approaches developed in other areas of NLP (Utiyama and Isahara, 2007;Cohn and Lapata, 2007;Wu and Wang, 2009;Snyder et al., 2009), the idea of taking advantage of other-language data has recently been applied to machine transliteration.


Such an approach has the advantage of being fast and not dependent on the training of any base system.

it achieves only 64.8% word accuracy, which is lower than any of the results in Table 4.


Especially derivational morphemes are hard to learn with pure data-driven methods with no knowledge about semantics and thus it can result in undersegmentation.

estonian corpus separates only inflectional morphemes which thus leads to higher recall.


An optimal application of the one-translation-per-discourse heuristic would thus group the rules based on the presence of one of those words.

in the C 1 variant, each of these rules would be counted separately because of differences that in some cases do not directly affect the choice of content words.


As a result, applying the one-translation-per-discourse heuristic improved the multi-reference BLEU score.

here is one of the cases where our feature hurt performance.


However, this requires physical presence of the two conversants in one location.

text chat between users over cell phones has become increasingly popular in the last decade.


The subjects were instructed to read the lines verbatim.

due to ASR errors, the subjects had to repeat or improvise few turns (about 10%) to sustain the dialog.


In some cases (32% , Table 4), coarse and fine-grained foci include the same words (e.g., It doesn't ::::::: always hurt [interpretation: it hurts sometimes]).

finegrained focus usually (68%) comprises fewer words.


Intuitively, ADVPs are fairly easy (they are short and coarse-grained and finegrained foci are often the same).

pp and SBAR are longer and only 44% and 32% of words belonging to the coarse grained focus belong to the fine-grained focus respectively.


Some of these differences are warranted in that certain target language phenomena are better captured by the native annotation.

differences such as choice of lexical versus functional head are more arbitrary.


It is their success that motivates building explicitly trained, linear-time pruning models.

while a greedy solution for arc-standard transition-based parsers can be computed in linear-time, Kuhlmann et al.


For example, a request for action is the first part of an adjacency pair and thus requires a response from the addressee, but declining the request is a valid response.

the utterer may formulate her request for action in a way that attempts to remove the option of declining it ("Come to my office now!").


Since SVMs optimize on training set accuracy to learn f , it performs better on balanced training sets.

our dataset is highly imbalanced (∼ 5% positive instances).


We then did cross validation for the ODP tagger using gold dialog acts for training and automatically tagged dialog acts for testing.

for our best performing feature set so far, this reduced the F score from 65.8 to 52.7.


Co-reference resolution has received a lot of attention.

as Eisenstein and Davis (2006) noted, most research on co-reference resolution has focused on written text.


Shallow-n grammars (de Gispert et al., 2010) were introduced to reduce over-generation in the Hiero translation model (Chiang, 2005) resulting in much faster decoding and restricting reordering to a desired level for specific language pairs.

shallow-n grammars require parameters which cannot be directly optimized using minimum error-rate tuning by the decoder.


The reordering glue rule facilitates reordering at the top-level.

this is still not sufficient to allow long-distance reordering as the shallow-decoding restricts the depth of the derivation.


In the computational linguistics community, several projects have attempted to determine the grade level of a text (2nd/3rd/4th/etc).

the education community typically makes finer distinctions in reading levels, with each grade being covered by multiple levels.


Document-only active learning also outperformed  standard passive learning, which is consistent with previous work.

for Movie Reviews (bottom), there is little difference among the three settings, and in fact models trained with DUALIST appear to lag behind active learning with documents.


In NLP, unsupervised learning typically implies optimization of a "bumpy" objective function riddled with local maxima.

one exception is IBM Model 1 (Brown et al., 1993) for word alignment, which is the only model commonly used for unsupervised learning in NLP that has a concave loglikelihood function.


3 is non-concave due to the presence of a product within a log .

if the tag transition probabilities p(y j | y j−1 ) are all constants and also do not depend on the previous tag y j−1 , then we can rewrite Eq.


Applied to (2), these rules would attach the gesture to "books" (a prosodically prominent item), also to "other books", "give you other books", "can give you other books" and even to "I can give you other books" (heads saturated with their arguments).

nothing licenses attachments to "I" or "give".


While different schemes have been proposed for annotating citations according to their function (Spiegel-Rosing, 1977;Nanba and Okumura, 1999;Garzone and Mercer, 2000), the only recent work on citation sentiment detection using a relatively large corpus is by Athar (2011).

this work does not handle citation context.


They model each sentence as a node in a graph and experiment with various window boundaries to create edges between neighbouring nodes.

their dataset consists of only 10 papers and their annotation scheme differs from our four-class annotation as they do not deal with any sentiment.


(Bollacker, 2008) contains an extensive database of names and nicknames 2 , with listings on over 13,000 given names, containing multiple "variations" for each name.

this database makes no attempt to distinguish between common and less common variants and skips some very common nicknames.


Similarly, WordNet does not indicate "jaguar" could be related to "car" at all.

the "car" sense of "jaguar" dominates the vector created using the search engine.


Many of these systems used techniques that exploited the specific aspects of the task, e.g., German-specific morphological analysis.

we present a knowledge-impoverished, entirely data-driven approach, by simply looking for more data in large collections.


Even though these can make HMMs easier to train and scale than more structured models such as BNs, it also puts them in a disadvantage concerning context-awareness and accuracy as shown by our results.

the random variables of BNs allow them to keep a structured model of the space, user, and relevant content selection and utterance planning choices.


There has been a substantial amount of research effort devoted to user generated content-related search tasks, including blog search, forum search, and community-based question answering.

there has been relatively little research on mi-croblog search.


Traditional query expansion approaches typically find terms that commonly co-occur with the query terms in documents (or passages).

such approaches are not suitable for expanding queries in the microblog setting since microblog messages are very short, yielding unreliable co-occurrence information.


Using this definition, the coverage score of a time interval is computed as: where tf w i ,T S is the term frequency of w i in timespan T S and β w is the expansion weight of term w. Since multiple events may occur at the same time, microblog streams can easily be dominated by the larger of two events.

less popular events may also exhibit burstiness at the same time.


Public events, such as federal elections involve people across the country.

a car pileup typically only attracts local attention.


Linear CRF achieved an accuracy 0.87, which is higher than the baseline of majority class predictor (N, 0.80) (ttest, p = 10 −10 ).

the precision and recall is low potentially because the tweets are short and noisy.


It shows that even simple features and off-the-shelf predicted as Tease Not  Tease  52  47  Not  26 559   Table 4: Confusion Matrix of Teasing Classification classifier can detect some signal in the text.


Some recovered topics, including the ones shown here, provide valuable insight into bullying traces.

not all topics are interpretable to social scientists.


There is considerable work on identifying the source of an opinion.

it is much harder to find obvious features that tell us whether "virtualization" is the target of an opinion.


It is empirically observed that contextualized word types can assume very few (most often, one) POS tags.

along with graph smoothness terms, they apply a penalty that encourages distributions to be close to uniform, the premise being that it would maximize the entropy of the distribution for a vertex that is far away or disconnected from a labeled vertex.


In particular, the E-step for EM can be written as where Q is the space of all distributions.

while EM produces a distribution in the E-step, hard EM is thought of as producing a single output given by one can also think of hard EM as producing a distribution given by q = δ(h = h * ).


Lastly, the range of γ from ∞ to 1 has been used in deterministic annealing for EM (Rose, 1998;Ueda and Nakano, 1998;Hofmann, 2001).

the focus of deterministic annealing is solely to solve the standard EM while avoiding local maxima problems.


We omit the graph for entity prediction because EM-based approaches do not outperform the supervised baseline there.

notably, for entities, for κ = 10%, UEM outperforms CoDL and PR and for 20%, the supervised baseline outperforms PR statistically significantly.


This is crucial in structured SVM, because solving the dual problem is cubic in terms of the number of examples and constraints.

our approach selects the slack such that at least one of the constraints is satisfied and adds all the remaining constraints to the active set.


When they work correctly, these tools allow users to maintain clear communication while potentially increasing the rate at which they input their message, improving efficiency in communication.

when these tools make a mistake, they can cause problematic situations.


Traditional spell checking systems generally assume that misspellings are unintentional.

much of the spelling variation that appears in text messages may be produced intentionally.


Note that, in this example, the word Tussaud could be an autocompletion or an autocorrection by the system.

there may be no significant distinction between these two operations from a user's point of view.


It is difficult to assess the appropriate precision-recall tradeoff without an in-depth study of autocorrection usage by text messagers.

a few observations can be made from the precision-recall curve.


The end-to-end system can reach a recall level of 0.674, significantly lower than the recall of the ground truth system.

the system still peaks at precision of 1, and was able to produce precision values that were competitive with the ground truth system at lower recall levels, maintaining a precision of above 0.90 until recall reached 0.396.


This may be true for a well-studied language like English, where we can easily compose a rule that disallows coreference between two mentions if they disagree in number and gender, for instance.

computing these features may not be as simple as we hope for a language like Chinese: the lack of morphology complicates the determination of number information, and the fact that most Chinese first names are used by both genders makes gender determination difficult.


Following common practice, we stemmed the parallel corpus using the Porter stemmer (Porter, 1980) in order to reduce data sparseness.

even with stemming, we found that many English words were not aligned to any French words by the resulting alignment model.


Nevertheless, one reason why this method is intuitively better is that it ensures that the training and test documents are drawn from the same domain.

when projecting annotations via a parallel corpus, we may encounter a domain mismatch problem if the parallel corpus and the test documents come from different domains, and the coreference resolver may not work well if it is trained and tested on different domains.


State features consider relating the label y (time-bin) of a single vertex (medical concept) to features corresponding to a medical concept x, and are given by, Transition features consider the mutual dependence of labels y i−1 and y i (dependence between the time-bins of the current and previous medical event in the sequence) and are given by, Above, s j is a state feature function, and λ j is its associated weight and t k is a transition function, and µ k is its associated weight.

to the state function, the transition function takes as input the current label as well as the previous label, in addition to the data.


For example, in a relatively structured scenario like compliance training, it may be better to reduce any possibility of confusion by eliminating false positives.

a self-motivated learner attempting to explore a new topic may tolerate a higher false positive rate in exchange for a broader diversity of questions.


One can  imagine automatically mining image/caption data (like that in Figure 1) to train object recognition systems.

in order to do so reliably, one must know whether the "car" actually appears or not.


Probabilistic word alignment models can induce bilexical distributions over target-language translations of source-language words (Brown et al., 1993).

word-to-word correspondences do not capture the full structure of a bilingual lexicon.


Uszkoreit and Brants 2008 (Diab and Resnik, 2002;Kaji, 2003;Ng et al., 2003;Tufis et al., 2004;Apidianaki, 2009) relates to our work in that these approaches discover word senses automatically through clustering, even using multilingual parallel corpora.

our task of clustering multiple words produces a different type of output from the standard word sense induction task of clustering in-context uses of a single word.


In theory, we could enable all 2 C possible component combinations, although we expect to use far less.

constraining the SCTM's topics by the components gives less flexible topics as compared to LDA.


On the other hand, constraining the SCTM's topics by the components gives less flexible topics as compared to LDA.

we find empirically that a large number of topics can be effectively modeled with a smaller number of components.


Representing bilingual sentences as a sequence of operations enables them to memorize phrases and lexical reordering triggers like PBSMT.

using minimal units during decoding and searching over all possible reorderings means that hypotheses can no longer be arranged in 2 m stacks.


Phrase-based SMT on the other hand overcomes these drawbacks by using larger translation chunks during search.

the drawback of the phrase-based model is the phrasal independence assumption, spurious ambiguity in segmentation and a weak mechanism to handle non-local reorderings.


We investigate the addition of MTUs to a phrasal translation system to improve modeling of context and to provide more robust estimation of long phrases.

in a phrase-based system there is no single synchronized traversal order; instead, we may consider the translation units in many possible orders: left-to-right or right-to-left according to either the source or the target are natural choices.


Unlike the maximum entropy model, we make no attempt to use entire phrases or phrasepairs as features, as they would be far too sparse for our small tuning sets.

due to the sparse features' direct decoder integration, we have access to a fair amount of extra context.


We can express this with constraints: After adding the constraints, the probability of the sequence is maximized when each word is assigned the tag with highest probability.


Therefore, these hard bilingual constraints guarantee that when two words are aligned, they are tagged with the same named entity tag.

in practice, aligned word pairs do not always have the same tag because of the difference in annotation standards across different languages.


This condition can be regarded as a kind of hard word alignment.

the following problem exists: the smaller the θ, the noisier the word alignments are; the larger the θ, the more possible word alignments are lost.


(2012) proposed a method of labeling bilingual corpora with named entity labels automatically based on Wikipedia.

this method is restricted to topics covered by Wikipedia.


Zhuang and Zong (2010) proposed a joint inference method for bilingual semantic role labeling with ILP.

their approach requires training an alignment model with a manually annotated corpus.


P&D acquired more content word variation pairs as the curves labeled by cwv indicates.

proposed Score 's precision outperformed p&D's by a large margin for the three languages.